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This paper details the implementation of an algorithm for automaticallygenerating a high-level knowledge network to perform commonsense reasoning,specifically with the application of robotic task repair. The network isrepresented using a Bayesian Logic Network (BLN) (Jain, Waldherr, and Beetz2009), which combines a set of directed relations between abstract concepts,including IsA, AtLocation, HasProperty, and UsedFor, with a correspondingprobability distribution that models the uncertainty inherent in theserelations. Inference over this network enables reasoning over the abstractconcepts in order to perform appropriate object substitution or to locatemissing objects in the robot's environment. The structure of the network isgenerated by combining information from two existing knowledge sources:ConceptNet (Speer and Havasi 2012), and WordNet (Miller 1995). This is done ina "situated" manner by only including information relevant a given context.Results show that the generated network is able to accurately predict objectcategories, locations, properties, and affordances in three different householdscenarios.

Situated Structure Learning of a Bayesian Logic Network for Commonsense Reasoning